[Topic-models] Comparison of different tools for LDA
radoslaw.kowalski.14 at ucl.ac.uk
Thu Jun 9 05:08:21 EDT 2016
I would discourage you from using R because it has few robust packages for deep learning. My opinion is that deep learning is likely going to be used a lot in topic modelling in the future. In where I am in UCL we often use gensim but the list of relevant python packages is much longer. Gensim is not always a golden solution to every topic model problem. You may find easier to use python packages for specific problems.
All the best,
Consumer Data Research Centre
UCL Department of Political Science
T: 020 3108 1098 x51098
E: radoslaw.kowalski.14 at ucl.ac.uk<mailto:n.vij at ucl.ac.uk>
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From: topic-models-bounces at lists.cs.princeton.edu <topic-models-bounces at lists.cs.princeton.edu> on behalf of Shayan A Tabrizi <shayantabrizi at gmail.com>
Sent: 08 June 2016 21:57:25
Subject: [Topic-models] Comparison of different tools for LDA
There are several tools for LDA. But I don't know which one is better and when? I wonder if anyone could guide me in choosing one toolbox. My priorities are ease-of-use and supporting various variations and extensions of LDA.
Some but not all of the candidates are:
1- MALLET (Java)
2- gensim (Python)
3- topicmodels (R)
4- Stanford Topic Modeling Toolbox
Thanks in advance,
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